Testing peatland water-table depth transfer functions using high-resolution hydrological monitoring data

AbstractTestate amoebae are a frequently used palaeoecological proxy for reconstructing changes in palaeohydrological conditions, particularly in studies of Sphagnum-dominated peatlands. Their use in palaeoecological studies has increased following the development of transfer functions, allowing for the quantitative reconstruction of water-table depth changes through time. Increasingly, they are included in non-pollen palynomorph (NPP) studies alongside a wide range of other proxies, representing a valuable tool, particularly in multi-proxy studies.Testate amoebae have been used for qualitative assessment of palaeohydrology in NPP studies and may aid the verification of environmental interpretations of conditions inferred from curves of NPP with unknown ecology and taxonomy. Their usefulness in such studies is limited by the destruction of tests owing to harsh chemical treatments used in pollen preparation methods. This makes community distribution data of testate amoebae derived by these methods largely unsuitable for quantitative assessment of water-table depth. Furthermore, many palynological studies combine testate amoebae as one single curve, losing further ecological detail. Patterns of change of surviving species, most commonly of Assulina, Archerella, Arcella, Hyalosphenia and Archerella flavum, remain relatively unaffected and therefore can still be useful for interpreting qualitative changes in hydrological conditions through time, particularly when coupled with other proxies.

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High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model

Frontiers in Water ◽

10.3389/frwa.2021.701726 ◽

2021 ◽

Vol 3 ◽

Author(s):

Julian Koch ◽

Jane Gotfredsen ◽

Raphael Schneider ◽

Lars Troldborg ◽

Simon Stisen ◽

...

Keyword(s):

High Resolution ◽

Water Table ◽

Spatial Resolution ◽

Shallow Groundwater ◽

Water Table Depth ◽

Training Data ◽

Gradient Boosting ◽

Detailed Knowledge ◽

Mitigation And Adaptation ◽

Physically Based

Detailed knowledge of the uppermost water table representing the shallow groundwater system is critical in order to address societal challenges that relate to the mitigation and adaptation to climate change and enhancing climate resilience in general. Machine learning (ML) allows for high resolution modeling of the water table depth beyond the capabilities of conventional numerical physically-based hydrological models with respect to spatial resolution and overall accuracy. For this, in-situ well and proxy observations are used as training data in combination with high resolution covariates. The objective of this study is to model the depth of the uppermost water table for a typical summer and winter condition at 10 m spatial resolution over entire Denmark (43,000 km2). CatBoost, a state of the art implementation of gradient boosting decision trees, is employed in this study to model the water table depth and the associated uncertainties. The groundwater domain has not been the most prominent field of applications of recent hydrological ML advances due to the lack of big data. This study brings forward a novel knowledge-guided ML framework to overcome this limitation by integrating simulation results from a physically-based groundwater flow model. The simulation data are utilized to (1) identify wells that represent the uppermost water table, (2) augment missing training data by accounting for simulated water level seasonality, and (3) expand the list of covariates. The curated training dataset contains around 13,000 wells, 19,000 groundwater proxy observations at lakes, streams and coastline as well as 15 covariates. Cross validation attests that the ML model generalizes well with a mean absolute error of around 115 cm considering solely well observations and a MAE of <50 cm taking also the proxy observations into consideration. Quantile regression is applied to estimate confidence intervals and the estimated uncertainty is largest for moraine clay soils that are characterized with a distinct geological heterogeneity. This study highlights a novel research avenue of knowledge-guided ML for the groundwater domain by efficiently supporting a ML model with a physically-based hydrological model to predict the depth of the water table at unprecedented spatial detail and accuracy.

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Effect of taxonomic resolution on ecological and palaeoecological inference – a test using testate amoeba water table depth transfer functions

Quaternary Science Reviews ◽

10.1016/j.quascirev.2014.03.006 ◽

2014 ◽

Vol 91 ◽

pp. 62-69 ◽

Cited By ~ 16

Author(s):

Edward A.D. Mitchell ◽

Mariusz Lamentowicz ◽

Richard J. Payne ◽

Yuri Mazei

Keyword(s):

Water Table ◽

Transfer Functions ◽

Water Table Depth ◽

Taxonomic Resolution ◽

Testate Amoeba

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Temporal and Local Heterogeneities of Water Table Depth under Different Agricultural Water Management Conditions

Water ◽

10.3390/w13162148 ◽

2021 ◽

Vol 13 (16) ◽

pp. 2148

Author(s):

Jonathan A. Lafond ◽

Silvio J. Gumiere ◽

Virginie Vanlandeghem ◽

Jacques Gallichand ◽

Alain N. Rousseau ◽

...

Keyword(s):

Water Management ◽

Water Table ◽

Cropping Systems ◽

Irrigation Management ◽

Sprinkler Irrigation ◽

Water Table Depth ◽

Management Systems ◽

Integrated Water Management ◽

Growing Seasons ◽

Good Drainage

Integrated water management has become a priority for cropping systems where subirrigation is possible. Compared to conventional sprinkler irrigation, the controlling water table can lead to a substantial increase in yield and water use efficiency with less pumping energy requirements. Knowing the spatiotemporal distribution of water table depth (WTD) and soil properties should help perform intelligent, integrated water management. Observation wells were installed in cranberry fields with different water management systems: Bottom, with good drainage and controlled WTD management; Surface, with good drainage and sprinkler irrigation management; Natural, without drainage, or with imperfectly drained and conventional sprinkler irrigation. During the 2017–2020 growing seasons, WTD was monitored on an hourly basis, while precipitation was measured at each site. Multi-frequential periodogram analysis revealed a dominant periodic component of 40 days each year in WTD fluctuations for the Bottom and Surface systems; for the Natural system, periodicity was heterogeneous and ranged from 2 to 6 weeks. Temporal cross correlations with precipitation show that for almost all the sites, there is a 3 to 9 h lag before WTD rises; one exception is a subirrigation site. These results indicate that automatic water table management based on continuously updated knowledge could contribute to integrated water management systems, by using precipitation-based models to predict WTD.

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Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-210066 ◽

2021 ◽

Vol 25 (2) ◽

pp. 227-234

Author(s):

Sandeep Samantaray ◽

Abinash Sahoo

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Water Table ◽

Environmental Sustainability ◽

Model Development ◽

Water Table Depth ◽

Water Levels ◽

Coefficient Of Determination ◽

Mean Square ◽

Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

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The distinct roles of water table depth and soil properties in controlling alternative woodland-grassland states in the Cerrado

Oecologia ◽

10.1007/s00442-021-04869-z ◽

2021 ◽

Author(s):

Jonathan W. F. Ribeiro ◽

Natashi A. L. Pilon ◽

Davi R. Rossatto ◽

Giselda Durigan ◽

Rosana M. Kolb

Keyword(s):

Soil Properties ◽

Water Table ◽

Water Table Depth

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Role of tree stand evapotranspiration in maintaining satisfactory drainage conditions in drained peatlands

Canadian Journal of Forest Research ◽

10.1139/x10-084 ◽

2010 ◽

Vol 40 (8) ◽

pp. 1485-1496 ◽

Cited By ~ 48

Author(s):

Sakari Sarkkola ◽

Hannu Hökkä ◽

Harri Koivusalo ◽

Mika Nieminen ◽

Erkki Ahti ◽

...

Keyword(s):

Bulk Density ◽

Water Table ◽

Interactive Effect ◽

Late Summer ◽

Water Table Depth ◽

Separate Analysis ◽

Tree Stand ◽

Stand Volume ◽

Linear Effect ◽

Subsequent Decrease

Ditch networks in drained peatland forests are maintained regularly to prevent water table rise and subsequent decrease in tree growth. The growing tree stand itself affects the level of water table through evapotranspiration, the magnitude of which is closely related to the living stand volume. In this study, regression analysis was applied to quantify the relationship between the late summer water table depth (DWT) and tree stand volume, mean monthly summertime precipitation (Ps), drainage network condition, and latitude. The analysis was based on several large data sets from southern to northern Finland, including concurrent measurements of stand volume and summer water table depth. The identified model demonstrated a nonlinear effect of stand volume on DWT, a linear effect of Ps on DWT, and an interactive effect of both stand volume and Ps. Latitude and ditch depth showed only marginal influence on DWT. A separate analysis indicated that an increase of 10 m3·ha–1 in stand volume corresponded with a drop of 1 cm in water table level during the growing season. In a subsample of the data, high bulk density peat showed deeper DWT than peat with low bulk density at the same stand volume.

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